A multi-agent system can reduce latency on complex tasks by executing work concurrently.
Several pioneering harness frameworks support multi-agent systems.
However, the scalability of current multi-agent harnesses is often constrained by a central orchestrator's capacity to allocate tasks and coordinate workers.
To address this limitation, we introduce Agensh, a scalable self-organized multi-agent harness without a central orchestrator:
concurrent workers execute a multi-agent cooperation loop, continuously gathering context, claiming and self-assigning sub-tasks, taking action and sharing findings, verifying results, and merging progress in an asynchronous manner.
The loop is supported by the agentic organization infrastructure comprising three components:
- a shared workspace holds proposed, ongoing, and completed work;
- a message interface lets workers communicate;
- shared context retains reusable findings and work intentions.
To test the scalability of Agensh, we evaluate it on the five hardest ProgramBench tasks with GPT-5.6-sol (high).
Scaling from 1 to 128 agents raises the mean final test-pass rate from 19.31% to 28.78%, an approximately 49% relative improvement.
Larger organizations reach comparable test-pass rates earlier.
On pandoc, scaling from 1 to 1,024 agents raises the final test-pass rate from 33.89% to 55.06%.
Worker trajectories further show that different forms of self-organized cooperation gradually emerges and standardizes as the organization grows.
These results reveal the number of agents as a new scaling dimension for multi-agent organizations to expand the frontier of general intelligence, offering a practical solution for complex tasks under hard latency constraints or time budgets.